# woe: Weigth of Evidence In woe: Computes Weight of Evidence and Information Values

## Description

Computes the Weight of Evidence and Information Value between Dependent and Independent variable.

## Usage

 `1` ```woe(Data, Independent, Continuous, Dependent, C_Bin, Bad, Good) ```

## Arguments

 `Data` : Name of Data Set `Independent` : Name of the Independent Variable `Continuous` : True if the variable is continuous, False if variable is Ordinal or Nominal `Dependent` : Name of the Targer Variable `C_Bin` : Count of Bins to be computed `Bad` : Which categorical variable do you want to be bad `Good` : Which categorical variable do you want to be good

WOE

## Value

Returns a DataSet with computed WoE and IV values on success or 0 on Failure

## Note

"woe" shows the log-odds ratio between between Goods and Bads. In the Bivalued Dependenet variable, one value represents Goods and others are bads. In Detail with an Example: Let Dependent varaible be ATTRITED (0,1) and Independent variable be TENURE where, 1-Attrited, 0-Non Attrited. If I wish to check WOE and IV of Tenure with ATTRITED to know if Tenure has an effect in getting attrited, Then good would be 1 and bad=0. If I wish to check WOE and IV of Tenure with ATTRITED to know if Tenure has an effect in not getting attrited, Then good would be 0 and bad=1.

## Author(s)

Sudarson Mothilal Thoppay

## Examples

 ```1 2``` ```woe(Data=mtcars,"cyl",FALSE,"am",10,Bad=0,Good=1) woe(Data=mtcars,"mpg",TRUE,"am",10,Bad=0,Good=1) ```

### Example output

```  BIN BAD GOOD TOTAL  BAD% GOOD% TOTAL%    WOE    IV BAD_SPLIT GOOD_SPLIT
1   4   3    8    11 0.158 0.615  0.344  135.9 0.621     0.273      0.727
2   6   4    3     7 0.211 0.231  0.219    9.1 0.002     0.571      0.429
3   8  12    2    14 0.632 0.154  0.438 -141.2 0.675     0.857      0.143
1   1 10.4 14.3   4    0     4 0.211 0.000  0.125  -Inf   Inf      1.00
2   2 14.7 15.2   3    1     4 0.158 0.077  0.125 -71.9 0.058      0.75
3   3 15.5 17.3   3    1     4 0.158 0.077  0.125 -71.9 0.058      0.75
4   4 17.8 19.2   4    0     4 0.211 0.000  0.125  -Inf   Inf      1.00
5   5 19.2 21.0   1    3     4 0.053 0.231  0.125 147.2 0.262      0.25
6   6 21.4 22.8   2    2     4 0.105 0.154  0.125  38.3 0.019      0.50
7   7 22.8 27.3   2    2     4 0.105 0.154  0.125  38.3 0.019      0.50
8   8 30.4 33.9   0    4     4 0.000 0.308  0.125   Inf   Inf      0.00
GOOD_SPLIT
1       0.00
2       0.25
3       0.25
4       0.00
5       0.75
6       0.50
7       0.50
8       1.00
```

woe documentation built on May 2, 2019, 9:18 a.m.